The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00
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Let’s face it—Tuberculosis isn’t just something from the history books. It’s very much a present-day issue, and the numbers are louder than ever. This Awareness of Tuberculosis Course cuts through the noise and brings clear, well-organised information to help learners understand how TB spreads, who’s at risk, and what steps can be taken to limit its impact. You won’t find fluff here—just the straight facts, smartly presented and easy to follow. Whether you’re looking to improve your knowledge for work or personal interest, this course keeps things simple, sharp, and relevant. You’ll explore the causes, symptoms, stages, and treatment approaches without needing to be a medical professional. From how TB behaves in different environments to the precautions people can take, this course keeps the science understandable and the content accessible. With each module, your awareness of TB becomes clearer, making you more confident in recognising and responding to this global health challenge. No lab coats or long lectures—just clear, accessible learning delivered online at your own pace. Key Features CPD Accredited FREE PDF + Hardcopy certificate Fully online, interactive course Self-paced learning and laptop, tablet and smartphone-friendly 24/7 Learning Assistance Discounts on bulk purchases Course Curriculum Module 01 : Introduction to Tuberculosis Module 02 : Epidemiology and Drug Resistance Module 03 : Diagnostic Methods for Tuberculosis Module 04 : Treatment of Tuberculosis Module 05 : TB Management and Control Strategies Module 06 : Prevention and Control of Tuberculosis Learning Outcomes: Understand the global impact of tuberculosis. Identify diagnostic methods for TB detection. Explore effective treatment approaches. Develop TB management and control strategies. Master prevention techniques for TB transmission. Acquire knowledge to contribute to TB awareness and control efforts. Accreditation This course is CPD Quality Standards (CPD QS) accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Healthcare professionals Public health workers Medical students Researchers NGO staff Policymakers Community health educators Career path Epidemiologist Clinical Researcher Public Health Analyst Medical Doctor Nonprofit Health Worker Health Policy Advisor Certificates Digital certificate Digital certificate - Included Once you've successfully completed your course, you will immediately be sent a FREE digital certificate. Hard copy certificate Hard copy certificate - Included Also, you can have your FREE printed certificate delivered by post (shipping cost £3.99 in the UK). For all international addresses outside of the United Kingdom, the delivery fee for a hardcopy certificate will be only £10. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.
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You?ll also gain practical experience in applying the concepts and techniques learned, solidifying your newly acquired skills and facilitating their direct application in real-world scenarios. You?ll exit this course empowered to create robust, scalable, and efficient Java-based applications that drive innovation and growth for your organization The Java Platform The Java Platform Lifecycle of a Java Program Responsibilities of JVM Documentation and Code Reuse Using the JDK Explain the JDK?s file structure Use the command line compiler to compile a Java class Use the command line Java interpreter to run a Java application class The IntelliJ Paradigm Introduce the IntelliJ IDE The Basics of the IntelliJ interface IntelliJ Projects and Modules Creating and running Java applications Writing a Simple Class Write a Java class that does not explicitly extend another class Define instance variables for a Java class Create object instances Primitives vs Object References Implement a main method to create an instance of the defined class Adding Methods to the Class Write a class with accessor methods to read and write instance variables Write a constructor to initialize an instance with data Write a constructor that calls other constructors of the class to benefit from code reuse Use the this keyword to distinguish local variables from instance variables Object-Oriented Programming Real-World Objects Classes and Objects Object Behavior Methods and Messages Inheritance, Abstraction, and Polymorphism Encapsulation Inheritance Method Overriding Polymorphism Essential Java Programming Essential Java Programming Language Statements Arithmetic operators Operators to increment and decrement numbers Comparison operators Logical operators Return type of comparison and logical operators Use for loops Switch Expressions Switch Expressions and yield Using Strings and Text Blocks Create an instance of the String class Test if two strings are equal Get the length of a string Parse a string for its token components Perform a case-insensitive equality test Build up a string using StringBuffer Contrast String, StringBuffer,and StringBuilder Compact Strings Text Blocks Specializing in a Subclass Constructing a class that extends another class Implementing equals and toString Writing constructors that pass initialization data to parent constructor Using instanceof to verify type of an object reference Pattern matching for instanceof Overriding subclass methods Safely casting references to a more refined type Fields and Variables Discuss Block Scoping Rules Distinguish between instance variables and method variables within a method Explain the difference between the terms field and variable List the default values for instance variables Final and Static fields and methods Local Variable type inference Using Arrays Declaring an array reference Allocating an array Initializing the entries in an array Writing methods with a variable number of arguments Records Data Objects in Java Introduce records as carrier of immutable data Defining records Java Packages and Visibility Use the package keyword to define a class within a specific package Discuss levels of accessibility/visibility Using the import keyword to declare references to classes in a specific package Using the standard type naming conventions Visibility in the Java Modular System Correctly executing a Java application class The Java modular system Defining Modules Inheritance and Polymorphism Write a subclass with a method that overrides a method in the superclass Group objects by their common supertype Utilize polymorphism Cast a supertype reference to a valid subtype reference Use the final keyword on methods and classes to prevent overriding Interfaces and Abstract Classes Define supertype contracts using abstract classes Implement concrete classes based on abstract classes Define supertype contracts using interfaces Implement concrete classes based on interfaces Explain advantage of interfaces over abstract classes Explain advantage of abstract classes over interfaces Static, default and private methods in interfaces Sealed classes Introduce Sealed classes The sealed and permits modifiers Sealed Interfaces Exception Handling Introduction to Exception Handling Introduce the Exception architecture Defining a try/catch blocks Checked vs Unchecked exceptions Exceptions Defining your own application exceptions Automatic closure of resources Suppressed exceptions Handling multiple exceptions in one catch Helpful Nullpointers Enhanced try-with-resources Java Developer's Toolbox Developing applications Introduce the wrapper classes Explain Autoboxing and Unboxing Converting String representations of primitive numbers into their primitive types Defining Enumerations Using static imports Deprecating methods Advanced Java Programming Introduction to Generics Generics and Subtyping Bounded Wildcards Generic Methods Legacy Calls To Generics When Generics Should Be Used Lambda Expressions and Functional Interface Understanding the concept of functional programming Writing lambda expressions Understanding functional interfaces Collections Provide an overview of the Collection API Review the different collection implementations (Set, List and Queue) Explore how generics are used with collections Examine iterators for working with collections Using Collections Collection Sorting Comparators Using the Right Collection Lambda expressions in Collections Bonus Topics: Time Permitting Streams Understanding the problem with collections in Java Thinking of program solutions in a declarative way Use the Stream API to process collections of data Understand the difference between intermediate and terminal stream operations Filtering elements from a Stream Finding element(s) within a Stream Collecting the elements from a Stream into a List takeWhile and dropWhile intermediate operations Collectors Using different ways to collect the items from a Stream Grouping elements within a stream Gathering statistics about numeric property of elements in a stream
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Certification of Clinical Psychology: Clinical Psychology Course After completing the Clinical Psychology: Clinical Psychology Course, you can instantly download your certificate for FREE. The hard copy of the certification will also be delivered to your doorstep via post, which will cost £13.99. Who is this course for? Clinical Psychology: Clinical Psychology Course Online This Clinical Psychology: Clinical Psychology Course is ideal for anyone looking to start a career in the field of psychology or counselling, which includes the following related professions: Child Psychologist CBT Therapist Counsellor Requirements Clinical Psychology: Clinical Psychology Course Online To enrol in this Clinical Psychology: Clinical Psychology Course, students must fulfil the following requirements: Good Command over English language is mandatory to enrol in our Clinical Psychology: Clinical Psychology Course. Be energetic and self-motivated to complete our Clinical Psychology: Clinical Psychology Course. Basic computer Skill is required to complete our Clinical Psychology: Clinical Psychology Course. If you want to enrol in our Clinical Psychology: Clinical Psychology Course, you must be at least 15 years old. Career path Clinical Psychology: Clinical Psychology Course Online This Clinical Psychology course will prepare you for a variety of career paths, including the following: Psychology Assistant - £20,000 to £30,000 per annum Psychologist - £35,000 to £45,000 per annum Psychiatrist - £45,000 to £60,000 per annum Psychologist - £45,000 to £50,000 per annum Clinical Psychologist - £40,000 to £45,000 per annum
The Actuary Online Course offers a comprehensive exploration of these scientific foundations, delving into deterministic and stochastic models, life insurance, annuities, and the complex interplay between taxation, inflation, and risk.
Overview The UK sees 1.3 MILLION food poisoning cases annually, with 276 outbreaks reported by the FSA between 2015-2020, affecting nearly 10,000 people. As a food business owner, restaurant manager, or catering provider, these Food Hygiene risks aren’t just statistics; they’re a direct threat to your reputation, finances, and customer trust. The food industry has no margin for error. Just one incident of foodborne illness can trigger a chain reaction of devastating consequences: irreversible damage to your reputation, crippling legal action under the Food Safety Act 1990, and most importantly, harm to the customers who trust you. In today’s regulated environment, where compliance is mandatory and scrutiny is intense, proper Food Hygiene and safety practices aren’t just best practice; they’re the foundation of a successful, sustainable food business. This comprehensive course on How to Improve Your Food Hygiene Rating will deepen your understanding of Food Hygiene. After successful completion of this Food Hygiene course you can acquire the required skills in this sector. This How to Improve Your Food Hygiene Rating comes with accredited certification which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. The How to Improve Your Food Hygiene Rating course provides the essential knowledge and skills to protect your business and customers. So, get ready to prepare your business for success and safety in all aspects of food production and distribution. Why Choose this Food Hygiene Training? Get a Free CPD Accredited Certificate upon completion of Food Hygiene course Get a free student ID card with Food Hygiene Training The Food Hygiene is affordable and simple to understand This Food Hygiene course is entirely online, interactive lesson with voiceover audio Lifetime access to the Food Hygiene course materials The Food Hygiene comes with 24/7 tutor support How will I get my Food Hygiene certificate? You may have to take a quiz or a written test online during or after the Food Hygiene course. After successfully completing the Food Hygiene course, you will be eligible for the certificate. Who is this Food Hygiene course for? There is no experience or previous qualifications required for enrolment on this How to Improve Your Food Hygiene Rating. It is available to all students, of all academic backgrounds. Requirements Our How to Improve Your Food Hygiene Rating is fully compatible with PC's, Mac's, Laptop,Tablet and Smartphone devices. This Food Hygiene course has been designed to be fully compatible on tablets and smartphones so you can access your course on wifi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having these various qualifications will increase the value in your CV and open you up to multiple sectors related to Food Hygiene. Course Curriculum 2 sections • 13 lectures • 06:08:00 total length •Module 01: The Food Hygiene Rating Scheme: 00:52:00 •Module 02: Personal Hygiene: 00:27:00 •Module 03: Contamination Hazards: 00:25:00 •Module 04: How to Control Contamination Hazards: 00:37:00 •Module 05: Food Preparation and Processing for Food Manufacturing: 00:25:00 •Module 06: Cleaning: 00:24:00 •Module 07: Waste Disposal, Cleaning and Disinfection: 00:32:00 •Module 08: Food Safety Management System: 00:41:00 •Module 09: Pest Management: 00:26:00 •Module 10: Food Premises and Equipment: The Design and Construction: 00:30:00 •Module 11: Supervision of Food Safety: 00:34:00 •Module 12: Food Safety Training for the Staff: 00:15:00 •Assignment - How to Improve Your Food Hygiene Rating: 00:00:00
The human resources management is the department assigned to ensure the welfare of employees including their wages, compensation, and benefits. It is best to implement an efficient payroll management to make sure employees could receive their salary on time. Developing payroll management skills is possible after taking this Level 3 Diploma in HR and Payroll Management. It is designed to help HR managers and staff to learn the different payroll systems and choose the best system perfect for the needs of the department. Course Highlights The price is for the whole course including final exam - no hidden fees Accredited Certificate upon successful completion at an additional cost Efficient exam system with instant results Track progress within own personal learning portal 24/7 customer support via live chat Level 3 Diploma in HR and Payroll Management has been given CPD accreditation and is one of the best-selling courses available to students worldwide. This valuable course is suitable for anyone interested in working in this sector or who simply wants to learn more about the topic. If you're an individual looking to excel within this field then Level 3 Diploma in HR and Payroll Management is for you. We've taken this comprehensive course and broken it down into several manageable modules which we believe will assist you to easily grasp each concept - from the fundamental to the most advanced aspects of the course. It really is a sure pathway to success. All our courses offer 12 months access and are designed to be studied at your own pace so you can take as much or as little time as you need to complete and gain the full CPD accredited qualification. And, there are no hidden fees or exam charges. We pride ourselves on having friendly and experienced instructors who provide full weekday support and are ready to help with any of your queries. So, if you need help, just drop them an email and await a speedy response. Furthermore, you can check the validity of your qualification and verify your certification on our website at any time. So, why not improve your chances of gaining professional skills and better earning potential. Assessment and Certification At the end of the course, you will be required to sit an online multiple-choice test. Your test will be assessed automatically and immediately so that you will instantly know whether you have been successful. After you have successfully passed the final exam, you will be able to order an Accredited Certificate of Achievement at an additional cost of £19 for a PDF copy and £29 for an original print copy sent to you by post or for both £39. Career Path Not only does our CPD and CiQ accredited course look good on your CV, setting you apart from the competition, it can be used as a stepping stone to greater things. Further advance your learning, launch a new career or reinvigorate an existing one. On successful completion of this course, you have the potential to achieve an estimated salary of £35,000. The sky really is the limit. Course Curriculum HR and Payroll Management - Updated Version Human Resource Management Introduction to Human Resources 00:16:00 Employee Recruitment and Selection Procedure 00:35:00 Employee Training and Development Process 00:19:00 Performance Appraisal Management 00:19:00 Employee Relations 00:15:00 Ensuring Health and Safety at the Workplace 00:16:00 Motivation and Counselling 00:19:00 Employee Termination 00:15:00 Employer Records and Statistics 00:11:00 Essential UK Employment Law 00:35:00 Payroll Management Module 1: Payroll Basics 00:10:00 Module 2: Company Settings 00:08:00 Module 3: Legislation Settings 00:07:00 Module 4: Pension Scheme Basics 00:06:00 Module 5: Pay Elements 00:14:00 Module 6: The Processing Date 00:07:00 Module 7: Adding Existing Employees 00:08:00 Module 8: Adding New Employees 00:12:00 Module 9: Payroll Processing Basics 00:11:00 Module 10: Entering Payments 00:12:00 Module 11: Pre-Update Reports 00:09:00 Module 12: Updating Records 00:09:00 Module 13: e-Submissions Basics 00:09:00 Module 14: Process Payroll (November) 00:11:00 Module 15: Employee Records and Reports 00:13:00 Module 16: Editing Employee Records 00:07:00 Module 17: Process Payroll (December) 00:12:00 Module 18: Resetting Payments 00:05:00 Module 19: Quick SSP 00:10:00 Module 20: An Employee Leaves 00:13:00 Module 21: Final Payroll Run 00:07:00 Module 22: Reports and Historical Data 00:08:00 Module 23: Year-End Procedures 00:09:00 HR and Payroll Management - Old Version HR & Leadership Leader and HR Management 00:30:00 Commitment and HR Management 01:00:00 Team Management 00:30:00 Build A Mastermind Group 02:00:00 People Recognition in HR Management 00:30:00 Performance, Goals and Management 00:30:00 Think Outside The Box 00:30:00 Be Passionate about Your Work 00:30:00 The Importance Of A Good Team Leader 00:30:00 Human Resource Management Module One - Getting Started 00:30:00 Module Two - Human Resources Today 01:00:00 Module Three - Recruiting and Interviewing 01:00:00 Module Four - Retention and Orientation 01:00:00 Module Five - Following Up With New Employees 01:00:00 Module Six - Workplace Health & Safety 01:00:00 Module Seven - Workplace Bullying, Harassment, and Violence 01:00:00 Module Eight - Workplace Wellness 01:00:00 Module Nine - Providing Feedback to Employees 01:00:00 Module Ten - Disciplining Employees 01:00:00 Module Eleven - Terminating Employees 01:00:00 Module Twelve - Wrapping Up 00:30:00 Managing Payroll What Is Payroll? 00:30:00 Principles Of Payroll Systems 01:00:00 Confidentiality And Security Of Information 00:30:00 Effective Payroll Processing 01:00:00 Increasing Payroll Efficiency 01:00:00 Risk Management in Payroll 00:30:00 Time Management 00:30:00 Personnel Filing 00:30:00 When Workers Leave Employment 01:00:00 Hiring Employees 00:30:00 Paye and Payroll for Employers 01:00:00 Tell HMRC about a New Employee 01:00:00 Net And Gross Pay 00:30:00 Statutory Sick Pay 00:30:00 Minimum Wage for Different types of Work 01:00:00 Mock Exam Mock Exam - Level 3 Diploma in HR and Payroll Management 00:30:00 Final Exam Final Exam - Level 3 Diploma in HR and Payroll Management 00:30:00